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Alexander Koltunov

GOES Early Fire Detection Project An Overview and Update. (Principal Investigator). Alexander Koltunov. akoltunov@ucdavis.edu. Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, Davis. TFRSAC-Spring 2013, NASA Ames, Monterrey Bay CA, 4/17/2013.

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Alexander Koltunov

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  1. GOES Early Fire Detection Project An Overview and Update (Principal Investigator) Alexander Koltunov akoltunov@ucdavis.edu Center for Spatial Technologies and Remote Sensing (CSTARS), University of California, Davis TFRSAC-Spring 2013, NASA Ames, Monterrey Bay CA, 4/17/2013

  2. GOES Early Fire Detection (GOES-EFD) System Objective: A low-cost and reliable capacity for systematic rapiddetection and initial confirmation of new ignitions at regional level • as a component of the USFS Active Fire Mapping program run by the FS RSAC • complement existing related products (WF-ABBA, MODIS/VIIRS Active Fire ) Detect new incidents consistently within first 1-2 hours, preferably before they are reported by conventional sources

  3. GOES-EFD system integration within USFS Active Fire Mapping program • Provide standardized geospatial EFD products • seasonal • regional • Integrate EFD into existing decision-making environment at dispatch centers and GACCs • EFD to improve situational awareness, response planning, confirmation for fires reported via other sources

  4. GOES-EFD effort: Structure and Participants Developers Team: UCD: Alex Koltunov, Wei Li, Elaine Prins, Susan Ustin RSAC: Brad Quayle, Brian Schwind CSUMB: Vince Ambrosia Sponsors Developers Supporting End-User Partners UC Davis FS RSAC CSUMB USDA FS UC Davis DHS CAL FIRE Los Angeles County Fire Department (LACoFD) San-Bernardino National Forest Federal Interagency Communications Center (FICC) Output Products • New ignition locations • Detection confidence • Metadata per incident • Performance stats over previous fire seasons R&D, Implementation, Validation, Integration Feedback, Requirements, Evaluations, Integration RSAC:Format, Merge, Distribute to First Responders GOES Images GOES-EFD Algorithm, Software, and Utility Programs Run @ RSAC and NASA Ames Landsat Images

  5. Where are we today? • Alpha-version (GOES-EFD ver. 0.2) ready • simulated real-time mode, • mainly Matlab implementation; • Case studies in CA indicate: • faster initial detection than the operational satellite algorithm • potential to provide the earliest alarm, time and again • Algorithm optimizations and tests underway (as resources permit) • GOES-EFD ver.03 near completion (July 2013) • with ~35% fewer false alarms

  6. GOES-EFD: Intended Schedule • 2013-15: Major development-test iterations, implementation, and integration: complete the GOES-EFD β-version • 2015:Deployment of GOES-EFD-β at USFS RSAC as a component of the FS Active Fire Mapping Program • 2015-2016: Near-real-time delivery to participating users; • initial training and evaluation by participating users • user feedback and performance documentation • follow-up optimizations • 2017: Post-Deployment system maintenance and enhancement • 2016-2017: Adaptation to GOES-R Advance Baseline Imager

  7. Geostationary Satellites (GOES East/West): Frequent, Low-Cost Imaging of Vast Territories GOES-East GOES-West GOES Imager (NOAA): • Viewing geometry – fixed • Visible + Thermal Infrared (TIR) images • @ ~15-30 min step (5-min during Rapid Scan) • Effective TIR pixel size ~ 6 x 4 km over CA

  8. Active Fire Monitoring vs. Early Fire Detection Primary Objectives are Related but Rather Different: *Wildfire Automated Biomass Burning Algorithm … and So Are the Optimal Algorithms GOES WF-ABBA, MODIS / VIIRS Active Fire, AVHRR GOES-EFD

  9. GOES-EFD is a tool specifically optimized for the objectives of Early Detection

  10. Blackbody Spectrum T=700 K T=600 K Radiance T=500 K T=320 K λ 12 μm 4 μm Thermal IR Physical Basis for Infrared Fire Detection background pixel fire pixel Burning Area 4 km 4 km Planck’s Law: Radiance ( λ) = B ( λ, T ) wavelength temperature fire R4μm>>R11μm Primary regions used for detection:Short-wave TIR (3 - 5 μm) Long-wave TIR(10 - 12 μm) soil R4μm ~ R11μm

  11. “Annette fire: GOES 4-μm Band vs. 11-μm Band 4 μm 11 μm high low

  12. GOES-EFD algorithmic principle: merge Temporal + Contextual information Multitemporal background prediction by Dynamic Detection Model: Training Stage Select Images Database of Optimal Basis Images Detection Stage Combine Past Images Compute Parameters Pre-processing compare Inspection Image Reference Image Post-processing Koltunov & Ustin S.L. (2007) Rem Sens Environ Koltunov,  Ben-Dor, & Ustin (2009) Int J of Rem Sens

  13. GOES-EFD ver. 0.2: Training and Preprocessing

  14. GOES-EFD ver. 0.2: Detection Stage

  15. Pixel-wise “Unfiltered” Fire Mask Fire Pixels “Frog” “Middle T” “Boundary” Rejected Thermal Anomalies “Tamarack” “Border/Beck” Cloud Snow

  16. From Pixels to Events (potential incidents) GOES-EFD target objects are New Incidents (multi-pixel, multi-frame objects) current frame past frame Fire Confidence high Event Tracker: Analyze the temporal evolution of spatially connected groups of fire pixels low An “old” event:do not report/ report as “re-detected” A “new” event: report this event

  17. Retrospective Assessment of Incident Detection Timeliness and Accuracy Very different from validating an Active Fire Product Official records are incomplete High resolution imagery is infrequent Not a trivial problem: Official wildfire incident records + Landsat-based burn detection While any kind of error in the database is possible, not all kinds of errors are equally probable Derive useful and reliable performance measures, despite uncertainties and biases in truth data Koltunov A., Ustin, S. L., Prins, E (2012) “On timeliness and accuracy of wildfire detection by the GOES WF-ABBA algorithm over California during the 2006 fire season”, Remote Sensing of Environment, v.127: 194-209

  18. Initial Experiment with fire season 2006 Central California • Sample #1: 13fires with known initial report HOUR • Sample #2: 25 fires with known initial report DATE • * Used wildfire incident databases from: • California Department of Forestry and Fire Protection (CAL FIRE) • Geospatial Multi-­Agency Coordination (GeoMAC) group • Include wildfire incident reports from: USFS, BLM, NPS, CAL FIRE, et al.

  19. Detection latency statistics:GOES-EFD ver.02 Detection latency relative to initial report from conventional sources “perfect” algorithm

  20. Performance statistics: GOES-EFD ver.02 GOES-EFD can provide the earliest alarm GOES-EFD tends to detect fires earlier than WF-ABBA

  21. Recent and ongoing activities toward GOES-EFD ver.03 • Targeting false positives due to: • Motion of undetected cloud pixels • Sub-optimal image registration across time • Analyzing spatial gradients and spatial context of temporal change • Correlation between spatial connectivity, size, and magnitude of thermal anomaly pixels? • Spatial filtering of the residuals after multitemporal anomaly detection of Long-wave TIR? • Second-dominant image motion and local misalignment tests • Preparing a large-area test

  22. Incidents with “wrong” combinations of Spatial, Spectral, and Temporal features • eliminated ~ 21% of false new incidents (at error prob.< 0.1%) Rejected False Alarm True (wildfire) Event BT-4μm DDM Background Prediction Residual BT-4μm DDM Background Prediction Residual

  23. Laplacian Spatio-Temporal Filters of Long-wave Thermal Band • eliminated ~ 17% of false new incidents (at error prob.< 0.1%)

  24. Improved Performance statistics: GOES-EFD ver.03 • eliminated ~ 35% of false new incidents (at error prob.< 0.2%) 51

  25. GOES-R Advanced Baseline Imager (2016) • Full disk coverage: every 15 minutes • Continental US coverage: every 5 minutes. • Spatial resolution : 2 km in TIR • A new channel at: 10.3 µm. • Fewer saturated pixels • When GOES-Ris available: • Mature, well tested GOES-EFD system • EFD-prepared, EFD-friendly user community • acceptance by scientific community

  26. Conclusions • We are moving forward, as resources permit… • Prospective users: • when you say you need Earlier Detection – you are helping speed up the development ! Stay tuned …

  27. GOES-EFD: Intended Schedule • 2013-15: Major development-test iterations, implementation, and integration: complete the GOES-EFD β-version • 2015:Deployment of GOES-EFD-β at USFS RSAC as a component of the FS Active Fire Mapping Program • 2015-2016: Near-real-time delivery to participating users; • initial training and evaluation by participating users • user feedback and performance documentation • follow-up optimizations • 2017: Post-Deployment system maintenance and enhancement • 2016-2017: Adaptation to GOES-R Advance Baseline Imager

  28. We gratefully acknowledge Dedicated Support by : UC Davis GOES Receiver infrastructure and data are provided by CIMIS (California Irrigation Management Information System) program http://wwwcimis.water.ca.gov/cimis and personally thank Kevin Guerrero, Mark Rosenberg CAL FIRE Jim Crawford LA County Fire Dept. Shawna Legarza, FICC Quinn Hart, UC Davis Mui Lay, George Sheer UC Davis 19

  29. Zoom 1 Zoom 2: Path-Row: 43-34 New burn detection in Landsat pairs RGB= bands (7,4,3) June 25 June 25 What if, there isa new burn near suspected false positive? zooms July 27 July 27 dNBR no true burns one true burn What if, no new burnsfound near suspected false positive? dNBR dNBR dNBRA dNBRA dNBRA

  30. Example: Rejecting 1 confident fire pixel of the 4-pixel “Annette” fire (2006/8/3) BT-4μm DDM Background Prediction Residual fr.671 t=2006.08.01.1645 “hot” anomaly dBT4-11 DDM Background Prediction Residual fr.671 t=2006.08.01.1645 “cold” anomaly

  31. Automatic Thermal Image Registration original Radiance ~4 μm registered

  32. Automatic Thermal Image Registration-2 Radiance ~4 μm original registered

  33. GOES Short-wave Thermal Band Video

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